Extending a neural network to classify new objects
Yes this is standard transfer learning. Using a trained model, we can freeze the first N hidden layers of a classifier, except for the last few. This will allow our previous relevant training to be retained whilst also being able learn new features and target new classes.
We will then initialize a new output layer that works our new context(i.e sigmoid, 1 node for binary classifier). Everything is now set to resume training on our new y_targets.
Take a look at this link for some more info on transfer learning.
If you want no perturbance to your past learning, I would recommend freezing all previous hidden layers and then tacking some additional ones on.